Classroom discourse is a core medium of instruction -- analyzing it can provide a window into teaching and learning as well as driving the development of new tools for improving instruction. We introduce the largest dataset of mathematics classroom transcripts available to researchers, and demonstrate how this data can help improve instruction. The dataset consists of 1,660 45-60 minute long 4th and 5th grade elementary mathematics observations collected by the National Center for Teacher Effectiveness (NCTE) between 2010-2013. The anonymized transcripts represent data from 317 teachers across 4 school districts that serve largely historically marginalized students. The transcripts come with rich metadata, including turn-level annotations for dialogic discourse moves, classroom observation scores, demographic information, survey responses and student test scores. We demonstrate that our natural language processing model, trained on our turn-level annotations, can learn to identify dialogic discourse moves and these moves are correlated with better classroom observation scores and learning outcomes. This dataset opens up several possibilities for researchers, educators and policymakers to learn about and improve K-12 instruction. The data and its terms of use can be accessed here: https://github.com/ddemszky/classroom-transcript-analysis

通过分析教室话语可窥探教学及学习背后的因素，本文介绍了迄今规模最大的数学类教室话语记录数据库，通过自然语言处理学习对话交互语言并探究其与优质教学评估及学生学习成果的关系，为研究者、教育者和政策制定者提供了参考价值。

NCTE记录：小学数学课堂记录数据集